The paper proposes an ontology-based multi-sensor data fusion model framework for the wide application of multi-sensor data fusion, which uses ontology as the semantics model of data in the feature level data fusion to solve the heterogeneous problem of multi-source data. In the framework, an effective data processing algorithm is presented to preserve a reliable confidence level for data in a dynamic environment based on the requirements of data timeliness in real-time data fusion systems. Considering the uncertainty of fuzzy information, Transferable Belief Model (TBM) is used in the decision level of data fusion to achieve multi-source heterogeneous distributed data fusion. Finally, the effectiveness of the fusion framework and algorithm is verified via an example instance of onboard sensors data fusion.
Data fusion Ontology Sensor data fusion
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